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6123REEDCOLLEGE PLACE PORTLAND OREGON 97202503-777-4616ROBERT@MRESEARCH.COM
McCulloughResearch
AnalysisoftheBalancing
EnergyMarket
February20,2009
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ExecutiveSummary
Balancing energy is the energy used to keep the system in balance. In a perfect world the
need for balancing energy is slight only occurring when some unusual event occurs. In re-
cent years ISO/RTOs from California to New England have established highly structured
balancing energy markets with complex bidding rules and difficult to follow (and often se-
cret) computations to produce prices. 1
Balancing energy markets frequently become the benchmark markets setting prices in the
larger markets at their very high levels.
Strange bidding often occurs in these markets. The term hockey stick bids has been
coined to describe the non-economic bidding never before seen elsewhere in our economy.
These hockey stick bids start reasonably enough, but soon transition to prices a hundred
times higher than common sense would expect.
This white paper attempts to explain why these bids are so frequent as well as the economics
behind their submission to the nations ISO/RTOs.
1 ISO stands for Independent System Operator. The ISO (also called a Regional Transmission Organization)is responsible for transmission system operations in a given geographic area.
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Contents
Executive Summary ............................................................................................................................. 1Contents ..................................................................................................................................... 2A Short History of Dispatching Resource to Meet Real Time Loads .......................................... 3Defining Competitive Real Time Markets ........................................................................................ 5Sca ity ..................................................................................................................................... 8rc Prices
....................................................................................................................... 11Ma et ................................................................................................................................... 10rk Failure ....................................................................................................................... 131. ERCOT .............O............... ................................................................................................................. 162. CAIS
................................................................................................................. 19
3. New York ISO ......
4. PJM .........................5. ISO New England ................................................................................................................ 22Demand and Supply in Real Time ................................................................................................... 26 Transparency ................................................................................................................................... 36R ecommendations ............................................................................................................................. 38
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AShort
History
of
Dispatching
Resource
to
Meet
Real
Time
LoadsBalancing Energy has a central role in each of the six existing Regional Transmission Or-
ganizations. While each RTO has implemented the concept in a different manner, the basic
concept is familiar to the industry. Like the need for inventory in a store, an electric system
requires the ability to adjust the system in real time. Since plant and transmission outages are
generally regarded as a greater concern than inadvertent over-generation, all electric systems
need generation on call to avoid service interruptions.
The balancing energy problem dates to the spirited debate between Nikola Tesla and Tho-
mas Edison concerning the technology proposed to deliver electricity. Tesla argued for al-
ternating current (AC) and Edison for direct current (DC). At the close of the nineteenth
century, AC offered more advantages and so became the basis for todays modern system,
while DC was adopted for high voltage (HV) transmission. However, an AC systems major
drawback is that it cannot be dispatched in real time. DC transmission lines, on the other
hand, are fully dispatchable, because an engineer can actually move electricity from point to
point over a DC line.
To avoid the dispatch problem with the AC system, electricity scheduling is planned well in
advance. Since the vast majority of electricity loads are quite predictable, many units can be
easily scheduled a week, a day, or even an hour in advance. In a perfect world no balancing
energy would be required. As we know, however, the world which we inhabit is several
steps below perfection.
Even in an ideal hour, generation schedules and loads will change with weather. These
changes are largely predictable, but there is always an irreducible difference between fore-
casts and actuals. The following chart shows a system load and the system forecast from the
California ISO. The dispatchers needed 500 megawatts for 15 minutes at 6:00 A.M.
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The illustration shows the relative inflexibility of real time changes, unlike the schedules
planned days or hours in advance. The ISOs operators did not have the time to increase
operations at a base-load unit (any base-load units can change their generating levels gradual-
ly: in the language of electricity, they can ramp up or down as required). For the short time
period displayed here, the operators best solution was to identify a base-load unit already
operating in the relevant geographical area and ask the utility owning it to ramp up the units
generation.
In traditional systems, the utility would have dispatched a unit or units from its spinning re-
serve. In the portion of California served by the California ISO, a unit was chosen from the
BEEP stack (BEEP stands for Balancing Energy Ex-Post). The choice of the unit is pri-
marily economic, although if transmission is constrained within the system, geographical
constraints may also figure into the selection.
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In real time the system is stiff generation choices are limited and load responses are in-
frequently sufficient to meet requirements. Generally, the RTOs have back-stopped their
balancing energy purchases with Reliability Must Run units (RMR) that can be dispatched di-
rectly without reference to economic dispatch.
DefiningCompetitiveRealTimeMarketsWhile there is no theoretical reason why meeting balancing energy requirements with a mar-
ket should not work as well as traditional dispatch, the record of real time markets is best
characterized as mixed. The central problem is market structure since real time markets tend
not to meet the definition of perfect competition:
1. Many buyers2. Many sellers3. Transparency4. Freedom of entry
5. Freedom of exit
Taken one at a time, it is easy to see why RTO real time markets experience frequent market
failures.
Many buyers: As a general rule, only the RTO itself is the buyer. This would naturally al-
low the RTO to exercise monopsonistic purchasing power, but the RTO is limited to accept-
ing the supply curve regardless of distortion. The problem is accentuated by the enormous
cost of disruption should the RTO be unable to buy additional energy, for example, to offset
a power outage.
Many sellers: Also as a general rule, the number of sellers is small and is able to exercise sub-
stantial market power. Each represents a minor part of the market. Hence, it is optimal for
the seller to price the electricity for sale at marginal cost. In most ISOs there are a small
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number of bidders and each bidder plays a major part in the market. In practice, prices vary
greatly from marginal cost. In different RTOs, market manipulation gambits have led to
oddities in pricing. In fact, in every American RTO, at least some non-economic bidding
occurs every day.
Transparency: To varying degrees, characteristics of transparency are largely missing from
the nations electricity markets. This has not stopped RTO supporters from hypocritically
proclaiming that transparency makes the manipulation of the electricity market easier to
identify and monitor. Elsewhere in the economy, commodity markets are characterized by:
published prices; bidders can shop openly among suppliers; non-economic outcomes are not
shielded from public/regulatory scrutiny. If the major bidders have substantially more in-
formation at hand than many of their competitors, true of nearly all U.S. restructured electric
power environments, then the large bidders can use the information to their advantage. This
advantage is further strengthened by the ability of the bidders to manipulate the demand
curve for balancing energy in many cases.
Freedom of entry: In electricity markets that operate in real time, there is effectively no free-
dom of entry. When the bid stack is exhausted, additional supplies can only be procured by
calling upon the RMR units available to the dispatchers.
Freedom of exit: By definition, an RTO cannot leave the market. Individual suppliers are
able to exit the market, but as with entry, the ability to change participation in the short-term
is limited.
Perfect competition has been studied extensively since the original work of Alfred Marshall
in the nineteenth century. In a perfectly competitive market the supply curve is the sum of
the marginal costs of each of the suppliers. If the marginal cost curve meets the demand
curve at a point above average total cost, new suppliers will enter the market and if less, sup-
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pliers will exit the market. Under perfect competition Price = Marginal Cost = Average To-
tal Cost. The following chart shows the classic graphical representation.2
In a perfectly competitive market no one supplier is able to change prices. This leads to a
simple demonstration that bids should reflect marginal cost. Given an existing perfectly
2 It is relatively easy to prove that any reasonable marginal cost curve, with price expressed as a function ofquantity, will intersect its corresponding average cost curve at the point of minimum average cost.
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competitive price, P, assume that a supplier with a marginal cost equal to C considers bid-
ding at a new price, P>C. There are three possible outcomes based on whether the existing
price P is below C, between C and the suppliers new bid price P>C, or above P>C.
If the competitive price P is below C, the supplier gains nothing from itsgambit because no one will pay more for the same product. The suppliersproduct will not be sold.
If the price is between C and P>C, the supplier will lose money. It wouldhave made P minus C for the product if it had simply bid at C. Instead, it
has not sold anything for the same reason as in the first case and would havebeen better off simply bidding at C.
If P is larger than P>C, the supplier gains nothing since the price has beenset by the marginal costs of another supplier and the supplier would havebeen better off simply bidding at the competitive price P.
Overall, the suppliers optimum profit-maximizing strategy under perfect competition is to
price the product at marginal cost.
ScarcityPricesA central defense of the non-economic prices that frequently take place in RTO real time
markets is that non-economic prices are required to attract new investment. This argument
has spawned many variants to manage its economic fallacies.
At the heart of the argument is a simple economic law that no energy only (often called mo-
nomic) market will produce enough revenue in the long term to pay for new investment.
The law arises from basic theory of competitive markets. If it is impossible to price capacity a central feature of monomic markets the shortfall caused by the missing capacity reve-
nues will create an ongoing shortfall.3 This is sometimes termed the missing money prob-
lem. Some advocates of these markets have gone so far as to recommend faking the re-
3 Looking for the Voom, Robert McCullough, June 26, 2007.
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quirements for balancing energy to raise prices enough to offset the missing money.4
Others argue that non-economic pricing represents scarcity rents. The scarcity rents argu-
ment is especially pernicious since it assumes that whenever non-economic bids are success-
ful in setting non-economic prices this is proof that shortage has occurred.
The North American Electric Reliability Corporation (NERC) publishes annual and seasonal
load resource analyses for most of North America. The question of scarcity is an engineer-
ing calculation, rigorously delineated and applied, that has a definitive answer. This has not
stopped advocates from rewriting the underlying data to meet their requirements. The
Western Market Crisis of 2000-2001 is a case in point. Despite official reports before, dur-
ing, and after the crisis that showed that no scarcity was present, defenders of non-economic
prices have simply created explanations out of whole cloth to explain the crisis. In several
instances they parroted Enrons claims. For example:
The Pacific Northwest Drought of 2000: Precipitation, temperature, andrun-off in the Pacific Northwest are the province of the Northwest RiverForecast Center, an office of the National Weather Service. The NWRFC
makes drought determinations on a monthly basis. These are published onthe Web and available to any interested party. No drought took place in2000. Advocates like William Hogan have repeated this claim again andagain knowing full well that it is simply untrue.
Rapid demand growth in California: California peak loads fell from 1998through 2001. Again, the statement that demand increased rapidly duringthis period is simply untrue. The authoritative statistics are found in theWECC 10 Year Plans that are publicly available on the Web.
High bids were due to emission costs: This untrue claim is all the more star-tling given that the actual prices paid for emissions are now public and thefacts can be checked empirically. Again, proponents of this explanation forhigh real time market prices have not reviewed the actual prices.
Since the advent of real time markets in California, the NERC studies have not indicated any
scarcity conditions in the U.S. on a planning basis. This is significant since it either indicates
4 Acting in Time: Regulating Wholesale Electricity Markets, William W. Hogan, May 8, 2007.
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that the advocates of non-economic pricing are incorrect or that NERC has been unable to
correctly calculate reliability conditions for the past decade.
MarketFailureEvidence from California, Texas, the Central Atlantic, and New York indicates that the high
prices are actually market failures (as mentioned, each RTO operates slightly differently; thus
the mechanics of market failure will differ):
California: The history of market failure is so well-known it hardly bears re-peating. Californias ex-post market faced manipulation on a variety of le-vels. These included unresponsive dispatch, fallacious bids, imaginary gener-ation, anti-trust, and imaginary loads.
Texas: The Texas PUC is pursuing TXU for exercising market power as apivotal supplier. TXU, the major player in ERCOTs real time market, isable to set real time prices whenever the remaining bids are insufficient tomeet demand.
Central Atlantic states: FERCs 2008 order concerning Edison Mission de-monstrates a situation in which a major market participant removed itselffrom the day ahead market by making uneconomic bids, thus dividing themarket among competitors.
New York: In July 2008, NYISO notified FERC that since January 2008 oneor several market participants had scheduled transactions on eight circuit-ous paths around Lake Erie. The practice was most prevalent in April andMay. The ISO determined that 80% of the power flow originating in NewYork and terminating in the PJM service territory traveled over the direct in-terface connection between New York and New Jersey, regardless of howindividuals scheduled its path. This means that the certain market partici-
pants were sending their power through the most expensive and congestedcorridors, but not paying the fees like everyone else. This mechanism did notjust allow them to avoid paying higher fees, but also meant the power theywere selling was garnering a higher price, because, as market participants in-creased the volume of power flow on the congested interfaces, the price forpower would rise, further adding to the profits of the sellers. 5 The estimated
5 Letter from U.S. Senator Charles Schumer to FERC Chair Joseph Kelliher, August 12, 2008.
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cost to New Yorkers could reach 290 million dollars (higher if the deception
was practiced prior to 2008).
The following charts trace anomalous bidding patterns in five of the six RTOs. In the sixth,
the Midwest ISO, there is insufficient data to show anomalous bids since MISO does not
report bids that have not been accepted in the market.
1. ERCOTIn ERCOT, two firms, Suez Energy Marketing NA Inc., and FPL Energy Power MarketingInc., provided bids at the price cap during the time period illustrated below. Between De-
cember 1, 2005 (starting date of our data) and March 1, 2007 (the date of increase in the
price cap from $1,000 to $1,500) Suez Energy Marketing submitted numerous bids at the
$1,000 cap andFPL Energy Power Marketing submitted one bid (on October 18, 2006 at2:00 pm) at the $1,000 cap. Between March 1, 2007 and March 1, 2008 (the date of increase
in the price cap from $1,500 to $2,250), while FPL Energy no longer submitted any non-
economic bids, Suez Energy submitted an even larger number of them at the new higher
$1,500 cap. From March 1, 2008 onward, Suez Energy was the only qualified scheduling ent-
ity still submitting bids at the current $2,250 price cap until June 2, 2008.
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0
500
1000
1500
2000
2500
01
Dec
05
22
Dec
05
12
Jan
06
02
Feb
06
23
Feb
06
16
Mar06
06
Apr06
27
Apr06
18
May
06
08
Jun
06
29
Jun
06
20
Jul06
10
Aug
06
31
Aug
06
21
Sep
06
12
Oct
06
02
Nov06
23
Nov06
14
Dec
06
04
Jan
07
25
Jan
07
15
Feb
07
08
Mar07
29
Mar07
19
Apr07
10
May
07
31
May
07
21
Jun
07
12
Jul07
02
Aug
07
23
Aug
07
13
Sep
07
04
Oct
07
25
Oct
07
15
Nov
07
06
Dec
07
27
Dec
07
17
Jan
08
07
Feb
08
28
Feb
08
20
Mar08
10
Apr08
01
May
08
22
May
08
12
Jun
08
03
Jul08
24
Jul08
14
Aug
08
04
Sep
08
25
Sep
08
16
Oct08
06
Nov
08
$/MWh
ERCOTHighestBids/Day(Source:https://pi.ercot.com/contentproxy/publicList?folder_id=10001838)
The great majority of non-economic bids during this period were submitted by just one
market participant.
FPLENERGY POWER
MARKETING
0.2%
SUEZENERGY MARKETING
NAINC
99.8%
ERCOTNonEconomicBidders(Source:https://pi.ercot.com/contentproxy/publicList?folder_id=10001838)
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A sample non-economic bid by Suez displays the characteristic hockey stick shape with
economic bids occurring at lower levels of generation and then a sudden transition to
$1,000/MWh.
0
200
400
600
800
1000
1200
0 50 100 150 200 250 300 350
$/MWh
Megawatts
BidCurveforSUEZEnergyMarketingNAInc.atERCOTonMarch5,2007at24:00(Source:https://pi.ercot.com/contentproxy/publicList?folder_id=10001838)
2. CAISOIn CAISO, a soft cap of $400/MWh was in place during the one-year period between
March 4, 2007 and March 3, 2008 we studied. A soft bid cap is one where the market partic-
ipants may submit bids above the bid cap if they can provide adequate justification based on
sellers costs, but with the understanding that such bids cannot set the market clearing pricethat will prevail.
Bids were submitted at the bid cap throughout the period. Interestingly, the figure below
indicates that there were four days when bids in excess of the bid cap were submitted, pre-
sumably with their economic justification. For instance, an astronomical bid of $1,000/MWh
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that was more than twofold above the effective cap was submitted by the bidder 153908
on January 31, 2008 at 17:00. There is no economic justification for such an extraordinarily
high bid.
Other anomalous bids exceeding the bid cap included six bids submitted at $500/MWh, five
bids at $450/MWh, and five more bids varying in price slightly above the cap. In this one-
year period, a total of seventeen bids were submitted above the cap.
0
200
400
600
800
1000
1200
4Mar07
13
Mar07
22
Mar07
31
Mar07
9Apr07
18
Apr07
27
Apr07
6May
07
15
May
07
24
May
07
2Jun
07
11
Jun
07
20
Jun
07
29
Jun
07
8Jul07
17
Jul07
26
Jul07
4Aug
07
13
Aug07
22
Aug07
31
Aug07
9Sep
07
18
Sep
07
27
Sep
07
6Oct07
15
Oct07
24
Oct07
2Nov
07
11
Nov
07
20
Nov
07
29
Nov
07
8Dec
07
17
Dec
07
26
Dec
07
4Jan
08
13
Jan
08
22
Jan
08
31
Jan
08
9Feb
08
18
Feb
08
27
Feb
08
$/MWh
CAISOHighestBids/Day(Source:http://oasis.caiso.com)
CAISOs non-economic bidders constitute a relatively large and diverse collection of sche-
duling coordinators, as demonstrated in the pie chart below. We also note that almost a
quarter of the non-economic bids in CAISO were submitted by scheduling coordinators
whose bidder IDs have not been identified in the publicly available bid information.
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Unknown
Bidders
25%
251032
16%
986305
14%
883530
12%
109608
8%
142467
4%
676399
2%
119176
2%
172128
2%
138766
2%
116800
2%
781310
1% 705305
1%
153908
1%
Other
Bidders
8%
CAISONon
Economic
Bidders
(Source:http://oasis.caiso.com)
Bids by bidder 986305 for generator 525599 resemble a hockey stick, rising moderately
for certain levels of generation and then increasing sharply to meet the bid cap:
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0
50
100
150
200
250
300
350
400
450
0 20 40 60 80 100 120 140 160
$/MWh
Megawatts
Bidder"986305"s
Bid
Curve
for
Generator
"525599"
onJuly15,2007at23:00inCAISO(Source:http://oasis.caiso.com)
3. NewYorkISONYISO had maximum bids of $1,000/MWh in every hour for the period from January 2006
through March 2008.
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55456180
40%
98797750
42%
99477750
18%
NYISONon
Economic
Bidders(Source:http://mis.nyiso.com/public/P27list.htm)
For example, the bid by bidder 55456180 for generator 56036180 on June 12, 2007 is a
classic hockey stick bid.
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0
200
400
600
800
1000
1200
0 10 20 30 40 50 60 70 80 9
$/MWh
Megawatts
Bidder"55456180"s
Bid
Curve
for
Generator
"56036180"
onJune12,2007at 13:00inNYISO(Source:http://mis.nyiso.com/public/P 27list.htm)
0
4. PJMPJMs bid data is badly damaged and difficult to interpret. Unique among the six RTOs,
PJM mixes together day ahead and hour ahead bids without labeling them. Discussions with
PJM personnel indicate that they have no explanation for this practice. To add to the prob-
lems, a number of the bids appeared to be highly unlikely. Some bidders appeared to be us-
ing the number 1,000 to terminate their supply curves or perhaps felt that they had
plants with outputs that actually were 1,000 megawatts. Finally, PJMs data is designed to
hide the identity of the bidders in an idiosyncratic fashion that is likely to only hide their
identities from non-participants.
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0
200
400
600
800
1000
1200
1Apr07
11
Apr07
21
Apr07
1May
07
11
May07
21
May
07
31
May
07
10
Jun
07
20
Jun
07
30
Jun
07
10
Jul07
20
Jul07
30
Jul07
9Aug
07
19
Aug
07
29
Aug
07
8Sep
07
18
Sep
07
28
Sep
07
8Oct07
18
Oct07
28
Oct07
7Nov
07
17
Nov
07
27
Nov
07
7Dec
07
17
Dec
07
27
Dec
07
6Jan
08
16
Jan
08
26
Jan
08
5Feb
08
15
Feb
08
25
Feb
08
6Mar08
16
Mar08
26
Mar08
PJMHighest
Bids/Day(Source:http://legacy.pjm.com/markets/jsp/unitbid.jsp)
A variety of individual bidders in PJM submitted non-economic bids in a wide variety of pat-
terns.
PJM decided on December 1, 2006 to discontinue posting in public the unique identifier
numbers that are assigned to the bidders.
Due to the current unavailability of this information, associating temporal scheduling pat-
terns with their bidders or tracing individual bidders bidding activities over time are no
longer possible. Therefore, the market concentration of non-economic bidding activity in
PJM has been represented on a generator level in the pie chart below. Since many genera-tors are owned by just a few market participants, this chart overstates the diversity of the
non-economic bids.
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AAEMDgM3Mi44MDAxMjExNw
7%
AAEMDgM3My44MDAxMjExNg
7%
AAEMDgM3NC44MDAxMjExNQ
7%
AgMBAgQxNTUuMzEzMzIxMDc
7%
BAEADwMuODAwMTIxMDk
7%
BAEKBwU5NC4zMTMzMjEwMQ
7%
BAEKBwU5Ni4zMTMzMjEwMw
7%BAEKBwU5NS4zMTMzMjEwMg7%
BAEKBwU5OC4zMTMzMjEwNQ
7%
BAEKBwU5OS4zMTMzMjEwNg
7%
BAEADgouODAwMTIxMDc
4%
BAEADwIuODAwMTIxMDg
4%
AwAOBgI4MDIuODAxMzIyMDE2%
AAEMDgQ3Mi44MDAxMjExNw
2%
AAEMDgQ3My44MDAxMjExNg
2%
AAEMDgQ3NC44MDAxMjExNQ
2%
AgMBAgMxNTUuMzEzMzIxMDc
2%
BAEKBwI5NC4zMTMzMjEwMQ
2%
BAEKBwI5Ni4zMTMzMjEwMw
2%
BAEKBwI5NS4zMTMzMjEwMg
2%
BAEKBwI5OC4zMTMzMjEwNQ
2%
BAEKBwI5OS4zMTMzMjEwNg
2%
BAEADwQuODAwMTIxMDk
2% BAEPBwIuNTEwNTIxNzA
0%
BAEPBQMuNTEwMjIxNDA
0%BAEMBgYuMTAxMDIyMzA
0%
BAEPBgMuNTEwMTIxMTA
0%
PJM
Non
Economic
Generators(Source:http://legacy.pjm.com/markets/jsp/unitbid.jsp)
In the following example, one plant increased its bid from less than $100/MWh to
$1,000/MWh over 22 megawatts.
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0
200
400
600
800
1000
1200
0 20 40 60 80 100 120
BidCurve
for
Generator
"AAEMDgM3My44MDAxMjExNg"
onApril1,2007inPJM(Source:http://legacy.pjm.com/markets/jsp/unitbid.jsp)
140
5. ISONewEnglandLike New York, ISONE also has bids equal to the bid cap every hour of every day.
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0
200
400
600
800
1000
1200
1Aug
07
13
Aug
07
25
Aug
07
6Sep
07
18
Sep
07
30
Sep
07
12
Oct07
24
Oct07
5Nov
07
17
Nov
07
29
Nov
07
11
Dec
07
23
Dec
07
4Jan
08
16
Jan
08
28
Jan
08
9Feb
08
21
Feb
08
4Mar08
16
Mar08
28
Mar08
9Apr08
21
Apr08
3May
08
15
May
08
27
May
08
8Jun
08
20
Jun
08
2Jul08
14
Jul08
26
Jul08
7Aug
08
19
Aug
08
31
Aug
08
$/MWh
ISONE
Highest
Bids/Day
(Source:http://isone.com/markets/hstdata/mkt_offer_bid/rt_energy/index.html)
Bids at the bid cap are split between two non-economic bidders in a pattern similar to that in
ERCOT.
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140603
49.5%985313
50.5%
ISONE
Non
Economic
Bidders
(http://isone.com/markets/hstdata/mkt_offer_bid/rt_energy/index.html)
Bidder 985313s non-economic supply curve for generator 90307 also represents the
classic hockey stick pattern.
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0
200
400
600
800
1000
1200
0 5 10 15 20 25 30
$/MWh
Megawatts
Bidder"985313"s
Bid
Curve
for
Generator
"90307"
onFebruary19,2008at18:00inISONE
(Source:http://isone.com/markets/hstdata/mkt_offer_bid/rt_energy/index.html)
While the existence of non-economic bids is often cited as proof that scarcity is a frequent
event in balancing energy markets, reliability reports from NERC and each of the regional
reliability councils tell another story. For the ten years that balancing energy markets have
been active in the six U.S. RTOs, the authoritative reliability council reports have not identi-
fied any cases of scarcity on a planning basis. Regardless of that fact, non-economic bids
occur continuously often in every hour.
Such a striking difference between actual behavior and economic theory deserves an expla-
nation. One possible explanation is the behavior of bidders in markets with a vertical supplycurve.
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DemandandSupplyinRealTimeBalancing energy markets differ in several ways from the traditional Marshallian supply and
demand chart shown below. The most important difference is that the demand curve is ver-
tical; an RTOs requirements are fixed. When balancing energy is required, it is for a very
short period in which the price will not change the quantity needed.
A vertical demand curve is unusual in economics. Few instances can be imagined other than
a market for fire protection after the fire has started, or for medical care after an individual is
diagnosed with an immediate, life-threatening medical condition. In these cases, there is no
upward bound on prices, since an individual is most likely willing to pay anything for help in
a fire or in the emergency room.
RTOs address this problem by setting price caps. In policy debates around the U.S., howev-
er, it is clearly understood that the price cap is setting pricing behavior. This reflects the dy-
namics of bidding when the level of demand is fixed.
The chart illustrates a simple example where five suppliers, each with comparable levels of
capacity, form a stair step supply curve. Each generator has a marginal cost just $10 per
megawatt-hour greater than its next more efficient competitor. The bell curve6 shows the
probability of the RTOs demand level hitting its portion of the supply curve.
6 The example calculations use the same 30% probability for the three middle suppliers and 5% for the highest-and lowest-cost suppliers, with the five probabilities adding up to one.
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Supplier E has a marginal cost of $50/MWh and can expect to be called upon only 5% of
the time. The majority of time, suppliers A through D will be called upon because their
marginal costs are considerably lower. By the same token, Supplier A, the most efficient ge-
nerator, can always expect to be called upon since no other supplier has a lower cost.
In this example, the RTOs expected price for balancing energy is the sum of the marginalcosts times the probability of a specific marginal cost setting the market price, or 5% x $10 +
30% x $20 + 30% x $30 + 30% x $40 + 5% x $50 = $30.00 per megawatt-hour.
Each of the suppliers can expect a profit except for Supplier E. Supplier A, of course, oper-
ates 100% of the time, and receives $20/MWh for each megawatt-hour it produces. Suppli-
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er B only operates 90% of the time. When Supplier B sells into the market the average price
is $31.05 and its profit is $11.05 per megawatt-hour. It is immediately apparent that Suppli-
ers A and E can profit considerably more with a better bidding strategy. Supplier Es situa-
tion is straightforward. Es plant is only dispatched 5% of the time, but when it is dis-
patched there is no alternative. This is the definition of a pivotal supplier. Supplier E can
raise the bid to $1,000/MWh the price cap in this hypothetical example since it will not
change the chances of being called upon.
Supplier A has a similar situation. Supplier A is always called upon because it is the least ex-
pensive supplier. The most conservative bid would be to raise the price it asks to just under
the level of Supplier Bs marginal cost. Then during periods when only Supplier As genera-
tion is needed, it will receive twice the revenue.
Both Supplier As and Supplier Es strategies are riskless because they are able to unilaterally
raise the price to the RTO during periods when they are the pivotal suppliers. Their new
bidding strategies can never lose money relative to the perfectly competitive supply curve.
Their new bidding strategies will have an immediate impact on the expected cost to the RTO
since the expected price for balancing energy will increase from $30.00 to $78/MWh.
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It is important to note that the change in bidding strategy for Supplier A and Supplier E is
good for all of the bidders, because the higher price for Supplier E is enjoyed by each of the
other suppliers in the 5% case when its generation sets the price.
The change in profits is highly significant. The profit for Supplier A rises from $20/MWh to
$68/MWh. The profit for Supplier B rises from $11.05 to $61.05/MWh. It is also impor-tant to note that the changes do not affect the market shares of any of the other suppliers.
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MarginalCostBidding
StrategicBidding
$
$100.00
$200.00
$300.00
$400.00
$500.00
$600.00
$700.00
$800.00
$900.00
$1,000.00
SupplierA Supplie rB Supplie rC Supplie rD Supplie rE
ProfitsWhen
Suppliers
A
and
EAdopt
Strategic
Bidding
Now consider Supplier D whose situation is fraught with difficulty. It would like to follow
Supplier E to the price cap, but if Supplier D sets its price to 1 cent under the price cap,
Supplier E could underbid Supplier D by 1 cent. In this case, Supplier E would be dis-
patched 35% of the time (5% when E is a pivotal supplier and 30% when D is underbid).
This is a classic problem in game theory. In its simplest formulation there are four possible
outcomes:
Outcome 1: D and E both bid at marginal cost. Supplier D makes 5% x ($50-$40) or
$.50. Supplier E makes nothing.
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Outcome 2: Supplier D bids at marginal cost and Supplier E bids at the price cap. Supplier
D makes $48 and Supplier E makes $47.50.
Outcome 3: Supplier D bids at the price cap and Supplier E bids at marginal cost. Supplier
D makes 5% x ($1,000 - $40) or $48.00 per megawatt-hour. Supplier E makes 5% x ($1,000
- $50) or $47.50.
Outcome 4: Both D and E bid as close to $1,000 per megawatt-hour as possible. Supplier
Ds profit is 35% x ($1,000 - $40) or $336 per megawatt-hour. Supplier Es profit is 35% x
($1,000 - $50) or $332.50.
This is a traditional game theory payoff matrix:
The classical answer is relatively easy. If both players are conservative, the least risk for
Supplier D is to choose to bid near the price cap. The least risk for Supplier E is also to bid
the price cap. If both firms are conservative, their behavior will set the RTO price to the
price cap 35% of the time.
In this case, the price will be set by the lowest cost bidder 5% of the time. The two next-
highest-cost bidders will also be bidding at marginal cost. The two highest-cost bidders will
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be setting the price at $1,000 35% of the time. The weighted average of market prices and
their probabilities are $366.00 per megawatt-hour.
A careful study of the payoff matrix, however, may well lead Supplier E to another strategy.
It would prefer an outcome where it bids just under the price cap when Supplier D bids the
price cap. Then Supplier E would receive the price cap, or a price just under the cap 35% of
the time, but Supplier D would only receive the price cap 5% of the time. This strategy is
likely to provoke the following response from Supplier D:
332.15$ 47.50$
335.65$ 335.65$
332.15$ 332.50$
48.00$ 336.00$
SupplierE
PriceCap $1 PriceCap
SupplierD
PriceCap
$1
PriceCap
If the two suppliers options were restricted to only these two prices, the answer would clear-
ly be option four where both suppliers always bid the max. What happens in the real world
where the two suppliers have a choice of bids ranging from their marginal cost all the way up
to the cap?
This game theory matrix has no easy solution. Supplier E prefers a price just below the cap
since it allows a chance that it can take Supplier Ds position in the supply curve. Supplier Dprefers a price just below the price cap as well. Obviously, this is an unstable situation where
each has an incentive to decrease prices ever so slightly to take advantage of the other.
Dynamic games of this type lead to mixed strategies where the expectation that Supplier E
will bid at the price cap is sufficient to keep Supplier D pricing at the price cap. The final
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just that, the level of concentration is not unduly high by RTO standards. The Hirschman
strategy depends on Suppler Es expectations of the behavior of Supplier D and vice versa.
A dynamic simulation of an optimal mixed strategy for Suppliers D and E can be modeled.
The following illustrates the result of a 1,000 iteration dynamic game where each has opti-
mized its bidding strategy based on its competitors past behavior:
6%
0%2%4%8%
10%12%14%16%
BidFrequency
DollarsperMegawatt
hour
MixedBiddingStrategiesforSuppliersDandE
SupplierD SupplierE
In this case, Supplier Ds bids average $580 per megawatt-hour and Supplier Es bids are
$584.04 per megawatt-hour. The situation is improved for the RTO because prices only av-
erage $219.00 per megawatt-hour in this example. It should be noted that no assumption has
been made of collusion. The hypothetical generators have acted in their own self-interest
with no communications with their competitors. Also, as can be seen by the difference in
the games analyzed above, collusion is a powerful force in markets where surveillance is mi-
nimal and data on bids and bidders is secret. While the hypothetical example analyzed here is
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nfortunately, the technical problems with balancing energy markets are not restricted to the
anipulatingtheSupplyCurvecentral feature of the Western Market Crisis was the shortage of prescheduled energy and
California a central feature of several Fat Boy schemes used by Enron and others was
California, this Fat Boy scheme involved thousands of megawatts. The FERC Final
Enrons use of the fat boy trading strategy did not set the market clearing price in
Herfindahl Index for this example is 2,000. Concentration indices for RTO market are often
significantly higher.
U
slope of the demand curve. A very serious problem is whether the level of demand is under
control of one or more market participants.
M
A
its necessary replacement in the real time market. Given the complexities of the electric sys-
tem, attempting to operate it in real time is both expensive and risky. During the 2000-2001
California crisis and the 2003 crisis in Texas, each system was effectively forced to drive
ahead of its headlights.
In
to purchase energy in the day ahead market and then to submit erroneous schedules for the
same energy in the real time market. The term for energy scheduled in error, inadvertent
energy, carries a negative connotation since it implies errors in dispatch and transmission
planning that add expense and reduce reliability.
In
Staff Report addressed the problem directly:
the Cal ISOs real-time market. Under California market rules, entities are price tak-ers for the amount of generation in excess of actual load; that is, they are paid theclearing price that is established in the Cal ISO market.29 Nevertheless, the submis-sion of false schedules, and the Cal ISOs encouragement of such fabrications tocircumvent the balanced schedule rule, would be prohibited under Staffs recom-mendations in the Initial Report. The Initial Report included a recommendation thatall tariffs for market-based rates include an express prohibition against submitting
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he problem lies in the inability of the RTO to police the complex engineering issues of real
he possibility of non-economic prices in real time markets poses an irresistible temptation
he situation in balancing energy markets is far more difficult. Since many participants in
Transparencyprising, therefore, that the calls for secrecy in bids, bidders, and bid resolution
T
time markets. At the height of the Western Market Crisis FERC dispatched a team to ask
the five merchant operators in the California market whether their abysmal reliability record
represented economic withholding. The team reported back that they could find no evi-
dence of withholding, even though availability of the units owned by the five merchant op-
erators only averaged 50% during high load periods. As we now know, a number of plants
were later implicated in such withholding activities, including one owned by Enron where
the orders to take the unit out of service were issued over a recorded telephone line.
T
to market traders. Metaphorically, it is the equivalent of running a jewelry store on the hon-
or principle. Clearly, diamonds and rubies are likely to tempt a customer to shoplift. For
that reason, jewelry store owners employ stringent safeguards to avoid giving shoppers the
temptation.
T
balancing energy markets are also major generators as well as being retailers that serve large
loads, the ability to affect the demand for balancing energy is always present. Simultaneous-
ly, the absence of a downward sloping demand curve provides little incentive for effective
competition. The combination of these factors makes non-economic results common even
when the chance of scarcity is exceedingly remote.
It is not sur
have been so strident. Across the U.S. it is common for bidding data, bidders, and the algo-
rithms used to resolve the bids to range from difficult to completely secret. The situation
ranges from the highly secretive PJM where the bidders are secret, the bids are reported in
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an unusable format, and the algorithm is secret, to ERCOT where most data is available af-
ter a sixty-day delay and only the algorithm is unknown.
The nature of the RTO process also makes the secret data available on an asymmetric basis.
Members of the relevant committees at the RTOs often have access to considerably more
information than market participants who are not represented on the committees. Addition-
ally, minutes from system operations committees are not protected by open document and
open meeting laws, so that major players have considerably more information about the na-
ture of the markets than minor players.
One interesting facet of the investigation into Enrons activities in ERCOT and the Califor-
nia ISO was the discovery that Enron frequently had preferential access to highly significant
market information at both RTOs. Even absent Enrons market manipulation activities in
both areas, its access to theoretically secret information gave Enron an enormous strategic
advantage.7
Lobbyists who energetically support secrecy in balancing energy markets argue that absent
secrecy conspirators will be able to share information. This is an odd argument. Informa-
tion sharing is a very common practice (as shown in the trader tapes that came to light dur-
ing the Enron investigation). Traders can easily coordinate bids, share privileged informa-
tion, and affect market outcomes without the permission or knowledge of the RTO.
The Enron/Powerex market sharing agreements in the Project Stanley scheme practiced
in Alberta explicitly involved sharing market information.8
The criminal antitrust risk was so
7 In December 2000, for example, CAISO decided to file imaginary transmission schedules to block exportsfrom California to neighboring states. The policy was only discovered in hearings of the Senate Select Com-mittee to Investigate Price Manipulation of the Wholesale Energy Market in 2003. Enron emails identifiedbriefings concerning this secret practice in the spring of 2001.8 Enrons internal analysis of Project Stanley appears in the information released during the Enron investiga-tion. Enrons Project Stanley PowerPoint is entitled Project Stanley - July 20th Review MeetingInitial market analysis, July 20, 1999.
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